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Demand prediction for urban air mobility using deep learning.

Faheem Ahmed¹, Muhammad Ali Memon¹, Khairan Rajab²

¹Department of Information Technology, University of Sindh, Jamshoro, Sindh, Pakistan.

Peerj. Computer Science

|December 13, 2024

Summary

This summary is machine-generated.

Urban air mobility (UAM) shows promise for short-distance travel. A deep learning model, particularly the Transformer, accurately forecasts UAM demand, aiding investment decisions.

Keywords:

Deep learning Demand of mobility Prediction Temporal data Urban air mobility

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Area of Science:

Transportation Science
Artificial Intelligence
Urban Planning

Background:

Urban air mobility (UAM) is an emerging transport concept for metropolitan areas.
Assessing market viability requires accurate demand forecasting.
Financial commitment for UAM deployment is a significant challenge.

Purpose of the Study:

To investigate the market's capacity to support UAM deployment.
To address the critical challenge of demand forecasting in UAM.
To evaluate deep learning models for temporal data prediction in this context.

Main Methods:

Proposed a deep learning model for temporal data forecasting.
Utilized a benchmark dataset of 150,000 records.
Compared LSTM, GRU, and Transformer models for UAM demand prediction.

Main Results:

The Transformer model demonstrated superior performance.
Achieved a Root Mean Square Error (RMSE) of 0.64.
Identified the Transformer as a high-performing model for UAM demand.

Conclusions:

Deep learning, specifically the Transformer model, is effective for UAM demand forecasting.
Accurate forecasting enables better analysis of UAM investment feasibility.
This research supports decision-making for UAM market viability.